kde working
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import itertools
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import os.path
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import pickle
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from collections import defaultdict
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from pathlib import Path
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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import quapy as qp
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from Retrieval.commons import RetrievedSamples, load_sample
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
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from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
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from quapy.data.base import LabelledCollection
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from os.path import join
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from tqdm import tqdm
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from result_table.src.table import Table
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"""
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"""
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data_home = 'data'
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datasets = ['continent', 'gender', 'years_category', 'relative_pageviews_category', 'num_sitelinks_category']
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param_grid = {'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]}
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# param_grid = {'C': np.logspace(-1, 1, 2)}
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classifiers = [
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('LR', LogisticRegression(max_iter=5000), param_grid),
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('SVM', LinearSVC(), param_grid)
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]
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def benchmark_name(class_name):
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return class_name.replace('_', '\_')
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table = Table(name=f'accuracy', benchmarks=[benchmark_name(d) for d in datasets])
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table.format.show_std = False
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table.format.stat_test = None
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table.format.lower_is_better = False
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for class_name, (cls_name, cls, grid) in itertools.product(datasets, classifiers):
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train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
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texts, labels = load_sample(train_data_path, class_name=class_name)
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tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
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Xtr = tfidf.fit_transform(texts)
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print(f'Xtr shape={Xtr.shape}')
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print('training classifier...', end='')
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classifier = GridSearchCV(
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cls,
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param_grid=grid,
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n_jobs=-1,
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cv=5,
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verbose=10
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)
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classifier.fit(Xtr, labels)
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classifier_acc = classifier.best_score_
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classifier_acc_per_fold = classifier.cv_results_['mean_test_score'][classifier.best_index_]
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print(f'[done] best-params={classifier.best_params_} got {classifier_acc:.4f} score, per fold {classifier_acc_per_fold}')
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table.add(benchmark=benchmark_name(class_name), method=cls_name, v=classifier_acc_per_fold)
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Table.LatexPDF(f'./latex/classifier_Acc.pdf', tables=[table])
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@ -8,118 +8,92 @@ from quapy.protocol import AbstractProtocol
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import json
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def load_sample(path, class_name, max_lines=-1):
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def load_sample(path, class_name):
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"""
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Loads a sample json as a dataframe and returns text and labels for
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the given class_name
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:param path: path to a json file
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:param class_name: string representing the target class
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:param max_lines: if provided and > 0 then returns only the
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first requested number of instances
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:return: texts and labels for class_name
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:return: texts, labels for class_name
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"""
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df = pd.read_json(path)
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text = df.text.values
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try:
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labels = df[class_name].values
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except KeyError as e:
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print(f'error in {path}; key {class_name} not found')
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raise e
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if max_lines is not None and max_lines>0:
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text = text[:max_lines]
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labels = labels[:max_lines]
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labels = df[class_name].values
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return text, labels
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class TextRankings:
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def get_text_label_score(df, class_name, vectorizer=None, filter_classes=None):
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text = df.text.values
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labels = df[class_name].values
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rel_score = df.score.values
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def __init__(self, path, class_name):
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self.obj = json.load(open(path, 'rt'))
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self.class_name = class_name
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if filter_classes is not None:
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idx = np.isin(labels, filter_classes)
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text = text[idx]
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labels = labels[idx]
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rel_score = rel_score[idx]
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def get_sample_Xy(self, sample_id, max_lines=-1):
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sample_id = str(sample_id)
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O = self.obj
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docs_ids = [doc_id for doc_id, query_id in O['qid'].items() if query_id == sample_id]
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texts = [O['text'][doc_id] for doc_id in docs_ids]
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labels = [O[self.class_name][doc_id] for doc_id in docs_ids]
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if max_lines > 0 and len(texts) > max_lines:
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ranks = [int(O['rank'][doc_id]) for doc_id in docs_ids]
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sel = np.argsort(ranks)[:max_lines]
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texts = np.asarray(texts)[sel]
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labels = np.asarray(labels)[sel]
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if vectorizer is not None:
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text = vectorizer.transform(text)
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return texts, labels
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order = np.argsort(-rel_score)
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return text[order], labels[order], rel_score[order]
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def filter_by_classes(X, y, classes):
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idx = np.isin(y, classes)
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return X[idx], y[idx]
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class RetrievedSamples(AbstractProtocol):
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class RetrievedSamples:
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def __init__(self,
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class_home: str,
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test_rankings_path: str,
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load_fn,
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vectorizer,
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class_name,
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max_train_lines=None,
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max_test_lines=None,
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classes=None
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):
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self.class_home = class_home
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self.test_rankings_df = pd.read_json(test_rankings_path)
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self.load_fn = load_fn
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self.vectorizer = vectorizer
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self.class_name = class_name
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self.max_train_lines = max_train_lines
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self.max_test_lines = max_test_lines
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self.classes=classes
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def __call__(self):
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tests_df = self.test_rankings_df
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class_name = self.class_name
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vectorizer = self.vectorizer
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for file in self._list_queries():
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texts, y = self.load_fn(file, class_name=self.class_name, max_lines=self.max_train_lines)
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texts, y = filter_by_classes(texts, y, self.classes)
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X = self.vectorizer.transform(texts)
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train_sample = LabelledCollection(X, y, classes=self.classes)
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# loads the training sample
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train_df = pd.read_json(file)
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Xtr, ytr, score_tr = get_text_label_score(train_df, class_name, vectorizer, filter_classes=self.classes)
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# loads the test sample
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query_id = self._get_query_id_from_path(file)
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texts, y = self._get_test_sample(query_id, max_lines=self.max_test_lines)
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texts, y = filter_by_classes(texts, y, self.classes)
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X = self.vectorizer.transform(texts)
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try:
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test_sample = LabelledCollection(X, y, classes=train_sample.classes_)
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yield train_sample, test_sample
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except ValueError as e:
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print(f'file {file} caused an exception: {e}')
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yield None, None
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sel_df = tests_df[tests_df.qid == int(query_id)]
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Xte, yte, score_te = get_text_label_score(sel_df, class_name, vectorizer, filter_classes=self.classes)
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yield (Xtr, ytr, score_tr), (Xte, yte, score_te)
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def _list_queries(self):
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return sorted(glob(join(self.class_home, 'training_Query*200SPLIT.json')))
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def _get_test_sample(self, query_id, max_lines=-1):
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df = self.test_rankings_df
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sel_df = df[df.qid==int(query_id)]
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texts = sel_df.text.values
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try:
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labels = sel_df[self.class_name].values
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except KeyError as e:
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print(f'error: key {self.class_name} not found in test rankings')
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raise e
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if max_lines > 0 and len(texts) > max_lines:
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ranks = sel_df.rank.values
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idx = np.argsort(ranks)[:max_lines]
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texts = np.asarray(texts)[idx]
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labels = np.asarray(labels)[idx]
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return texts, labels
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# def _get_test_sample(self, query_id, max_lines=-1):
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# df = self.test_rankings_df
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# sel_df = df[df.qid==int(query_id)]
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# return get_text_label_score(sel_df)
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# texts = sel_df.text.values
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# try:
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# labels = sel_df[self.class_name].values
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# except KeyError as e:
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# print(f'error: key {self.class_name} not found in test rankings')
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# raise e
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# if max_lines > 0 and len(texts) > max_lines:
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# ranks = sel_df.rank.values
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# idx = np.argsort(ranks)[:max_lines]
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# texts = np.asarray(texts)[idx]
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# labels = np.asarray(labels)[idx]
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# return texts, labels
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def total(self):
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return len(self._list_queries())
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@ -130,4 +104,6 @@ class RetrievedSamples(AbstractProtocol):
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qid = path
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qid = qid[:qid.index(posfix)]
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qid = qid[qid.index(prefix) + len(prefix):]
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return qid
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return qid
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import os.path
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import pickle
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from collections import defaultdict
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from pathlib import Path
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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import quapy as qp
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from Retrieval.commons import RetrievedSamples, load_sample
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
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from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
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from quapy.data.base import LabelledCollection
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from os.path import join
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from tqdm import tqdm
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from result_table.src.table import Table
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"""
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In this sixth experiment, we have a collection C of >6M documents.
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We split C in two equally-sized pools TrPool, TePool
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I have randomly split the collection in 50% train and 50% split. In each split we have approx. 3.25 million documents.
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We have 5 categories we can evaluate over: Continent, Years_Category, Num_Site_Links, Relative Pageviews and Gender.
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From the training set I have created smaller subsets for each category:
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100K, 500K, 1M and FULL (3.25M)
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For each category and subset, I have created a training set called: "classifier_training.json". This is the "base" training set for the classifier. In this set we have 500 documents per group in a category. (For example: Male 500, Female 500, Unknown 500). Let me know if you think we need more.
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To "bias" the quantifier towards a query, I have executed the queries (97) on the different training sets and retrieved the 200 most relevant documents per group.
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For example: (Male 200, Female 200, Unknown 200)
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Sometimes this is infeasible, we should probably discuss this at some point.
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You can find the results for every query in a file named:
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"training_Query-[QID]Sample-200SPLIT.json"
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Test:
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To evaluate our approach, I have executed the queries on the test split. You can find the results for all 97 queries up till k=1000 in this file.
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testRanking_Results.json
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"""
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def methods(classifier, class_name):
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kde_param = {
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'continent': 0.18,
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'gender': 0.12,
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'years_category':0.09
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}
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yield ('Naive', Naive())
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yield ('NaiveQuery', Naive())
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yield ('CC', ClassifyAndCount(classifier))
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# yield ('PCC', PCC(classifier))
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# yield ('ACC', ACC(classifier, val_split=5, n_jobs=-1))
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yield ('PACC', PACC(classifier, val_split=5, n_jobs=-1))
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# yield ('EMQ', EMQ(classifier, exact_train_prev=True))
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# yield ('EMQ-Platt', EMQ(classifier, exact_train_prev=True, recalib='platt'))
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# yield ('EMQh', EMQ(classifier, exact_train_prev=False))
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# yield ('EMQ-BCTS', EMQ(classifier, exact_train_prev=True, recalib='bcts'))
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# yield ('EMQ-TS', EMQ(classifier, exact_train_prev=False, recalib='ts'))
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# yield ('EMQ-NBVS', EMQ(classifier, exact_train_prev=False, recalib='nbvs'))
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# yield ('EMQ-VS', EMQ(classifier, exact_train_prev=False, recalib='vs'))
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# yield ('KDE001', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.001))
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# yield ('KDE005', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
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# yield ('KDE01', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.01))
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# yield ('KDE02', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.02))
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# yield ('KDE03', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.03))
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# yield ('KDE-silver', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth='silverman'))
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# yield ('KDE-scott', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth='scott'))
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yield ('KDE-opt', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=kde_param[class_name]))
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yield ('KDE01', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.01))
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yield ('KDE02', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.02))
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yield ('KDE03', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.03))
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yield ('KDE04', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.04))
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yield ('KDE05', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.05))
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yield ('KDE07', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.07))
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# yield ('KDE10', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.10))
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def train_classifier(train_path):
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"""
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Trains a classifier. To do so, it loads the training set, transforms it into a tfidf representation.
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The classifier is Logistic Regression, with hyperparameters C (range [0.001, 0.01, ..., 1000]) and
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class_weight (range {'balanced', None}) optimized via 5FCV.
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:return: the tfidf-vectorizer and the classifier trained
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"""
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texts, labels = load_sample(train_path, class_name=class_name)
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tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
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Xtr = tfidf.fit_transform(texts)
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print(f'Xtr shape={Xtr.shape}')
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print('training classifier...', end='')
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classifier = LogisticRegression(max_iter=5000)
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classifier = GridSearchCV(
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classifier,
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param_grid={'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]},
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n_jobs=-1,
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cv=5
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)
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classifier.fit(Xtr, labels)
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classifier = classifier.best_estimator_
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classifier_acc = classifier.best_score_
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print(f'[done] best-params={classifier.best_params_} got {classifier_acc:.4f} score')
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training = LabelledCollection(Xtr, labels)
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print('training classes:', training.classes_)
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print('training prevalence:', training.prevalence())
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return tfidf, classifier
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def reduceAtK(data: LabelledCollection, k):
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# if k > len(data):
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# print(f'[warning] {k=}>{len(data)=}')
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X, y = data.Xy
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X = X[:k]
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y = y[:k]
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return LabelledCollection(X, y, classes=data.classes_)
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def benchmark_name(class_name, k):
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scape_class_name = class_name.replace('_', '\_')
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return f'{scape_class_name}@{k}'
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def run_experiment():
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results = {
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'mae': {k: [] for k in Ks},
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'mrae': {k: [] for k in Ks}
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}
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pbar = tqdm(experiment_prot(), total=experiment_prot.total())
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for train, test in pbar:
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Xtr, ytr, score_tr = train
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Xte, yte, score_te = test
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if HALF:
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n = len(ytr) // 2
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train_col = LabelledCollection(Xtr[:n], ytr[:n], classes=classifier_trained.classes_)
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else:
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train_col = LabelledCollection(Xtr, ytr, classes=classifier_trained.classes_)
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if method_name not in ['Naive', 'NaiveQuery']:
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quantifier.fit(train_col, val_split=train_col, fit_classifier=False)
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elif method_name == 'Naive':
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quantifier.fit(train_col)
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test_col = LabelledCollection(Xte, yte, classes=classifier_trained.classes_)
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for k in Ks:
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test_k = reduceAtK(test_col, k)
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if method_name == 'NaiveQuery':
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train_k = reduceAtK(train_col, k)
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quantifier.fit(train_k)
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estim_prev = quantifier.quantify(test_k.instances)
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mae = qp.error.mae(test_k.prevalence(), estim_prev)
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||||
mrae = qp.error.mrae(test_k.prevalence(), estim_prev, eps=(1. / (2 * k)))
|
||||
|
||||
results['mae'][k].append(mae)
|
||||
results['mrae'][k].append(mrae)
|
||||
|
||||
pbar.set_description(f'{method_name}')
|
||||
|
||||
return results
|
||||
|
||||
|
||||
data_home = 'data'
|
||||
|
||||
HALF=True
|
||||
exp_posfix = '_half'
|
||||
|
||||
method_names = [name for name, *other in methods(None, 'continent')]
|
||||
|
||||
Ks = [5, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
|
||||
|
||||
for class_name in ['gender', 'continent', 'years_category']: # 'relative_pageviews_category', 'num_sitelinks_category']:
|
||||
tables_mae, tables_mrae = [], []
|
||||
|
||||
benchmarks = [benchmark_name(class_name, k) for k in Ks]
|
||||
|
||||
for data_size in ['10K', '50K', '100K', '500K', '1M', 'FULL']:
|
||||
|
||||
table_mae = Table(name=f'{class_name}-{data_size}-mae', benchmarks=benchmarks, methods=method_names)
|
||||
table_mrae = Table(name=f'{class_name}-{data_size}-mrae', benchmarks=benchmarks, methods=method_names)
|
||||
table_mae.format.mean_prec = 5
|
||||
table_mae.format.remove_zero = True
|
||||
table_mae.format.color_mode = 'global'
|
||||
|
||||
tables_mae.append(table_mae)
|
||||
tables_mrae.append(table_mrae)
|
||||
|
||||
class_home = join(data_home, class_name, data_size)
|
||||
# train_data_path = join(class_home, 'classifier_training.json')
|
||||
# classifier_path = join('classifiers', data_size, f'classifier_{class_name}.pkl')
|
||||
train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl') # <------------ fixed classifier
|
||||
test_rankings_path = join(data_home, 'testRanking_Results.json')
|
||||
results_home = join('results'+exp_posfix, class_name, data_size)
|
||||
|
||||
tfidf, classifier_trained = qp.util.pickled_resource(classifier_path, train_classifier, train_data_path)
|
||||
|
||||
experiment_prot = RetrievedSamples(
|
||||
class_home,
|
||||
test_rankings_path,
|
||||
vectorizer=tfidf,
|
||||
class_name=class_name,
|
||||
classes=classifier_trained.classes_
|
||||
)
|
||||
for method_name, quantifier in methods(classifier_trained, class_name):
|
||||
|
||||
results_path = join(results_home, method_name + '.pkl')
|
||||
if os.path.exists(results_path):
|
||||
print(f'Method {method_name=} already computed')
|
||||
results = pickle.load(open(results_path, 'rb'))
|
||||
else:
|
||||
results = run_experiment()
|
||||
|
||||
os.makedirs(Path(results_path).parent, exist_ok=True)
|
||||
pickle.dump(results, open(results_path, 'wb'), pickle.HIGHEST_PROTOCOL)
|
||||
|
||||
for k in Ks:
|
||||
table_mae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mae'][k])
|
||||
table_mrae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mrae'][k])
|
||||
|
||||
# Table.LatexPDF(f'./latex{exp_posfix}/{class_name}{exp_posfix}.pdf', tables=tables_mae+tables_mrae)
|
||||
Table.LatexPDF(f'./latex{exp_posfix}/{class_name}{exp_posfix}.pdf', tables=tables_mrae)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
import itertools
|
||||
import os.path
|
||||
import pickle
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
import quapy as qp
|
||||
from Retrieval.commons import RetrievedSamples, load_sample
|
||||
from quapy.protocol import UPP
|
||||
from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
|
||||
from quapy.model_selection import GridSearchQ
|
||||
from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
|
||||
from quapy.data.base import LabelledCollection
|
||||
|
||||
from os.path import join
|
||||
from tqdm import tqdm
|
||||
|
||||
from result_table.src.table import Table
|
||||
|
||||
"""
|
||||
|
||||
"""
|
||||
|
||||
data_home = 'data'
|
||||
|
||||
datasets = ['continent', 'gender', 'years_category'] #, 'relative_pageviews_category', 'num_sitelinks_category']
|
||||
|
||||
for class_name in datasets:
|
||||
|
||||
train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
|
||||
texts, labels = load_sample(train_data_path, class_name=class_name)
|
||||
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl')
|
||||
tfidf, classifier_trained = pickle.load(open(classifier_path, 'rb'))
|
||||
classifier_hyper = classifier_trained.get_params()
|
||||
print(f'{classifier_hyper=}')
|
||||
|
||||
X = tfidf.transform(texts)
|
||||
print(f'Xtr shape={X.shape}')
|
||||
|
||||
pool = LabelledCollection(X, labels)
|
||||
train, val = pool.split_stratified(train_prop=0.5, random_state=0)
|
||||
q = KDEyML(LogisticRegression())
|
||||
classifier_hyper = {'classifier__C':[classifier_hyper['C'], 0.00000001], 'classifier__class_weight':[classifier_hyper['class_weight']]}
|
||||
quantifier_hyper = {'bandwidth': np.linspace(0.01, 0.2, 20)}
|
||||
hyper = {**classifier_hyper, **quantifier_hyper}
|
||||
qp.environ['SAMPLE_SIZE'] = 100
|
||||
modsel = GridSearchQ(
|
||||
model=q,
|
||||
param_grid=hyper,
|
||||
protocol=UPP(val, sample_size=100),
|
||||
n_jobs=-1,
|
||||
error='mrae',
|
||||
verbose=True
|
||||
)
|
||||
modsel.fit(train)
|
||||
|
||||
print(class_name)
|
||||
print(f'{modsel.best_params_}')
|
||||
print(f'{modsel.best_score_}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
import os.path
|
||||
import pickle
|
||||
from collections import defaultdict
|
||||
from itertools import zip_longest
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
from Retrieval.commons import RetrievedSamples, load_sample
|
||||
from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
|
||||
from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
|
||||
from quapy.protocol import AbstractProtocol
|
||||
from quapy.data.base import LabelledCollection
|
||||
|
||||
from glob import glob
|
||||
from os.path import join
|
||||
from tqdm import tqdm
|
||||
|
||||
from result_table.src.table import Table
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
"""
|
||||
Plots the distribution of (predicted) relevance score for the test samples and for the training samples wrt:
|
||||
- training pool size (100K, 500K, 1M, FULL)
|
||||
- rank
|
||||
"""
|
||||
|
||||
|
||||
data_home = 'data'
|
||||
Ks = [5, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
|
||||
|
||||
for class_name in ['num_sitelinks_category', 'relative_pageviews_category', 'years_category', 'continent', 'gender']:
|
||||
test_added = False
|
||||
Mtrs, Mtes, source = [], [], []
|
||||
for data_size in ['10K', '50K', '100K', '500K', '1M', 'FULL']:
|
||||
|
||||
class_home = join(data_home, class_name, data_size)
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl')
|
||||
test_rankings_path = join(data_home, 'testRanking_Results.json')
|
||||
|
||||
_, classifier = pickle.load(open(classifier_path, 'rb'))
|
||||
|
||||
experiment_prot = RetrievedSamples(
|
||||
class_home,
|
||||
test_rankings_path,
|
||||
vectorizer=None,
|
||||
class_name=class_name,
|
||||
classes=classifier.classes_
|
||||
)
|
||||
|
||||
Mtr = []
|
||||
Mte = []
|
||||
pbar = tqdm(experiment_prot(), total=experiment_prot.total())
|
||||
for train, test in pbar:
|
||||
Xtr, ytr, score_tr = train
|
||||
Xte, yte, score_te = test
|
||||
Mtr.append(score_tr)
|
||||
Mte.append(score_te)
|
||||
|
||||
Mtrs.append(Mtr)
|
||||
if not test_added:
|
||||
Mtes.append(Mte)
|
||||
test_added = True
|
||||
source.append(data_size)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
train_source = ['train-'+s for s in source]
|
||||
Ms = list(zip(Mtrs, train_source))+list(zip(Mtes, ['test']))
|
||||
|
||||
for M, source in Ms:
|
||||
M = np.asarray(list(zip_longest(*M, fillvalue=np.nan))).T
|
||||
|
||||
num_rep, num_docs = M.shape
|
||||
|
||||
mean_values = np.nanmean(M, axis=0)
|
||||
n_filled = np.count_nonzero(~np.isnan(M), axis=0)
|
||||
std_errors = np.nanstd(M, axis=0) / np.sqrt(n_filled)
|
||||
|
||||
line = ax.plot(range(num_docs), mean_values, '-', label=source, color=None)
|
||||
color = line[-1].get_color()
|
||||
ax.fill_between(range(num_docs), mean_values - std_errors, mean_values + std_errors, alpha=0.3, color=color)
|
||||
|
||||
|
||||
ax.set_xlabel('Doc. Rank')
|
||||
ax.set_ylabel('Rel. Score')
|
||||
ax.set_title(class_name)
|
||||
|
||||
ax.legend()
|
||||
|
||||
# plt.show()
|
||||
os.makedirs('plots', exist_ok=True)
|
||||
plotpath = f'plots/{class_name}.pdf'
|
||||
print(f'saving plot in {plotpath}')
|
||||
plt.savefig(plotpath)
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
import pandas as pd
|
||||
|
||||
from os.path import join
|
||||
|
||||
from Retrieval.commons import load_json_sample
|
||||
from quapy.data import LabelledCollection
|
||||
|
||||
data_home = 'data'
|
||||
CLASS_NAME = 'continent'
|
||||
datasize = '100K'
|
||||
|
||||
file_path = join(data_home, CLASS_NAME, datasize, 'training_Query-84Sample-200SPLIT.json')
|
||||
|
||||
text, classes = load_json_sample(file_path, CLASS_NAME)
|
||||
|
||||
|
||||
data = LabelledCollection(text, classes)
|
||||
print(data.classes_)
|
||||
print(data.prevalence())
|
||||
print('done')
|
||||
|
||||
test_ranking_path = join(data_home, 'testRanking_Results.json')
|
||||
# obj = json.load(open(test_ranking_path))
|
||||
|
||||
|
||||
df = pd.read_json(test_ranking_path)
|
||||
print('done')
|
||||
|
|
@ -141,6 +141,19 @@ def uniform_prevalence_sampling(n_classes, size=1):
|
|||
return u
|
||||
|
||||
|
||||
def uniform_prevalence(n_classes):
|
||||
"""
|
||||
Returns a vector representing the uniform distribution for `n_classes`
|
||||
|
||||
:param n_classes: number of classes
|
||||
:return: np.ndarray with all values 1/n_classes
|
||||
"""
|
||||
assert isinstance(n_classes, int) and n_classes>0, \
|
||||
(f'param {n_classes} not understood; must be a positive integer representing the '
|
||||
f'number of classes ')
|
||||
return np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
|
||||
|
||||
uniform_simplex_sampling = uniform_prevalence_sampling
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -62,7 +62,13 @@ class KDEBase:
|
|||
:param bandwidth: float, the bandwidth of the kernel
|
||||
:return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates
|
||||
"""
|
||||
return [self.get_kde_function(X[y == cat], bandwidth) for cat in classes]
|
||||
class_cond_X = []
|
||||
for cat in classes:
|
||||
selX = X[y==cat]
|
||||
if selX.size==0:
|
||||
selX = [F.uniform_prevalence(len(classes))]
|
||||
class_cond_X.append(selX)
|
||||
return [self.get_kde_function(X_cond_yi, bandwidth) for X_cond_yi in class_cond_X]
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
|
|||
from copy import deepcopy
|
||||
from typing import Callable, Union
|
||||
import numpy as np
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling, PlattScaling
|
||||
from scipy import optimize
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.calibration import CalibratedClassifierCV
|
||||
|
|
@ -636,20 +636,35 @@ class EMQ(AggregativeSoftQuantifier):
|
|||
calibrator = TempScaling()
|
||||
elif self.recalib == 'vs':
|
||||
calibrator = VectorScaling()
|
||||
elif self.recalib == 'platt':
|
||||
calibrator = CalibratedClassifierCV(estimator=self.classifier, cv='prefit')
|
||||
else:
|
||||
raise ValueError('invalid param argument for recalibration method; available ones are '
|
||||
'"nbvs", "bcts", "ts", and "vs".')
|
||||
|
||||
if not np.issubdtype(y.dtype, np.number):
|
||||
y = np.searchsorted(data.classes_, y)
|
||||
self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
|
||||
|
||||
if self.recalib == 'platt':
|
||||
self.classifier = calibrator.fit(*data.Xy)
|
||||
else:
|
||||
print(classif_predictions.prevalence())
|
||||
try:
|
||||
self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
|
||||
except RuntimeError as e:
|
||||
print(e)
|
||||
print('defaults to I')
|
||||
self.calibration_function = lambda P:P
|
||||
|
||||
if self.exact_train_prev:
|
||||
self.train_prevalence = data.prevalence()
|
||||
else:
|
||||
train_posteriors = classif_predictions.X
|
||||
if self.recalib is not None:
|
||||
train_posteriors = self.calibration_function(train_posteriors)
|
||||
if self.recalib == 'platt':
|
||||
train_posteriors = self.classifier.predict_proba(train_posteriors)
|
||||
else:
|
||||
train_posteriors = self.calibration_function(train_posteriors)
|
||||
self.train_prevalence = F.prevalence_from_probabilities(train_posteriors)
|
||||
|
||||
def aggregate(self, classif_posteriors, epsilon=EPSILON):
|
||||
|
|
|
|||
Loading…
Reference in New Issue